Population-Egypt 1950-2018

import numpy as np
import pandas as pd

import plotly.express as px
%matplotib.inline

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)

UsageError: Line magic function `%matplotib.inline` not found.

data = pd.read_csv('population-egypt.csv')
data

	Year	Total	Male	Femal	Rural	Urban
0	1950	20451978	10381760	10070228	14099948	6613383
1	1951	20948601	10638072	10310528	14320220	6894794
2	1952	21473798	10909309	10564483	14553593	7193215
3	1953	22028134	11195254	10832878	14800269	7508829
4	1954	22611510	11495505	11115997	15058949	7842649
...	...	...	...	...	...	...
64	2014	90424656	45697164	44727504	52480985	39331581
65	2015	92442547	46717678	45724871	53654843	40123329
66	2016	94447073	47729770	46717301	54799311	40889370
67	2017	96442591	48735775	47706815	55893406	41659745
68	2018	98423598	49733474	48690128	56938212	42437529

69 rows × 6 columns

data.shape

(69, 6)

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 69 entries, 0 to 68
Data columns (total 6 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   Year    69 non-null     int64
 1   Total   69 non-null     int64
 2   Male    69 non-null     int64
 3   Femal   69 non-null     int64
 4   Rural   69 non-null     int64
 5   Urban   69 non-null     int64
dtypes: int64(6)
memory usage: 3.4 KB

data.describe()

	Year	Total	Male	Femal	Rural	Urban
count	69.000000	6.900000e+01	6.900000e+01	6.900000e+01	6.900000e+01	6.900000e+01
mean	1984.000000	5.191973e+07	2.622694e+07	2.569278e+07	3.048664e+07	2.230249e+07
std	20.062403	2.284973e+07	1.149585e+07	1.135436e+07	1.277513e+07	1.046895e+07
min	1950.000000	2.045198e+07	1.038176e+07	1.007023e+07	1.409995e+07	6.613383e+06
25%	1967.000000	3.205651e+07	1.630321e+07	1.575330e+07	1.939054e+07	1.314348e+07
50%	1984.000000	4.796864e+07	2.414941e+07	2.381923e+07	2.740480e+07	2.146415e+07
75%	2001.000000	7.015266e+07	3.532456e+07	3.482810e+07	4.071151e+07	3.051543e+07
max	2018.000000	9.842360e+07	4.973347e+07	4.869013e+07	5.693821e+07	4.243753e+07

data.isnull().sum()

Year     0
Total    0
Male     0
Femal    0
Rural    0
Urban    0
dtype: int64

#renaming column: Female
data.rename({'Femal':'Female'}, axis = 1, inplace = True)

Visualizing Population

fig = px.line(x=data['Year'], y=data['Total'], title='Egypt Population 1950-2018', labels={'x':'Year', 'y':'Population'})

fig.show()

import plotly.graph_objects as go

months = data.Year

fig = go.Figure()
fig.add_trace(go.Bar(
    x=data.Year,
    y=data.Total,
    name='Population',
    marker_color='indianred'
))


# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()

Population by gender

import plotly.graph_objects as go

random_x = data.Year
random_y0 = data.Male
random_y1 = data.Female

fig = go.Figure()
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
                    mode='lines',
                    name='Male'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
                    mode='lines',
                    name='Female'))


fig.show()

#stacked bar chart

fig = go.Figure()
fig.add_trace(go.Bar(
    x=data.Year,
    y=data.Male,
    name='Male',
    marker_color='blue'
))
fig.add_trace(go.Bar(
    x=data.Year,
    y=data.Female,
    name='Female',
    marker_color='red'
))

# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()

Population by geographic area

random_y3 = data.Rural
random_y4 = data.Urban


fig = go.Figure()
fig.add_trace(go.Scatter(x=random_x, y=random_y3,
                    mode='lines',
                    name='Rural'))
fig.add_trace(go.Scatter(x=random_x, y=random_y4,
                    mode='lines',
                    name='Urban'))


fig.show()